Working hours: EU business hours, shifted 2–3 hours later to ensure daily overlap with US West Coast (PST/PDT) mornings About the Client Our client is a leading enterprise data platform company building an open, high-performance data lakehouse for AI and analytical workloads. The platform combines an intelligent SQL query engine, an AI-ready semantic layer, and an open catalog built on Apache Iceberg — enabling Fortune 500 companies across finance, energy, manufacturing, and logistics to unify, query, and govern data at massive scale across cloud and on-premise sources. About the Role We are looking for a Senior SDET to lead the design of automated test frameworks and testing strategy for a large-scale distributed data platform. You will own the quality of the query engine, connectivity layer, and backend services end-to-end — from framework architecture through CI/CD infrastructure across all major clouds — and mentor middle engineers on the team.
This is a data-heavy, backend-focused role. We are not looking for web/UI QA engineers — the work centers on validating distributed query execution, data correctness at scale, connectivity drivers, and backend microservices. Responsibilities * Architect, design, and evolve automated test frameworks in Python / pytest for backend services, REST APIs, and distributed data components. * Define testing strategy for new features and platform components — coverage, risk assessment, and quality gates. * Own end-to-end, integration, performance, and regression testing covering SQL query execution, data correctness at scale, and platform APIs. * Lead performance and load testing efforts with JMeter, including workloads over JDBC / ODBC / Arrow Flight drivers. * Design and validate large-scale data testing scenarios — query plans, result correctness across heterogeneous data sources, metadata consistency, and behavior under high concurrency. * Own CI/CD test pipelines in Jenkins — architect, maintain, and continuously improve. * Provision and manage test environments in Kubernetes (GKE / EKS / AKS) across GCP, AWS, and Azure using Docker; drive automation and reproducibility. * Investigate complex, cross-layer failures and drive root-cause analysis with engineering. * Mentor middle SDETs, review test designs, and set quality standards across the team. * Partner with US-based development leads on shift-left practices, testability, and release readiness.
Required Qualifications * Education: B.S. or M.S. in Computer Science, Computer Engineering, or a related technical field. * Programming: Strong proficiency in Python, including pytest, with deep understanding of OOP, software design principles, and test framework architecture. * SQL & Data: Advanced SQL skills and strong understanding of relational and analytical data systems, including query execution internals. * Data-intensive testing experience: Demonstrated experience testing data-intensive systems — query engines, ETL/ELT pipelines, streaming platforms, or analytical databases. Candidates with only web/UI QA backgrounds are not a fit. * Testing experience: 5+ years in backend/system test automation, with demonstrated ownership of test strategy and infrastructure. * CI/CD & DevOps: Solid experience architecting and maintaining Jenkins pipelines and test environments. * Containers & Orchestration: Strong working knowledge of Docker and Kubernetes (running workloads, debugging pods, deploying complex environments). * Cloud: Hands-on experience with at least one major cloud (GCP, AWS, or Azure); exposure to more than one is a strong plus. * Version Control: Confident with Git / GitHub workflows and code review practices. * English: Upper-Intermediate or higher (B2+) — daily written and verbal communication with a US-based engineering team. * Availability: Able to work EU hours with a 2–3 hour shift toward US West Coast time to ensure daily overlap with the client team. * Leadership: Prior experience mentoring engineers, defining test strategy, or leading a QA/SDET function.
Desired Skills * Deep experience testing REST APIs and backend microservices at scale. * Hands-on with distributed computing frameworks (e.g., Apache Spark, Kafka) and MPP SQL query engines (e.g., Presto, Trino, or similar). * Strong understanding of modern data lakehouse concepts, open table formats (Apache Iceberg), and data warehousing. * Experience with data connectivity drivers: JDBC, ODBC, Arrow Flight. * Performance testing with JMeter or comparable load-testing tools at production scale. * Kubernetes on managed services (GKE / EKS / AKS) and multi-cloud exposure. * IaC tools such as Terraform. * Deep understanding of query plan generation, query acceleration / materializations, and metadata integrity in distributed data systems. * Prior experience leading a QA/SDET function in a data-platform or database engineering environment.
